惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

量子位
D
Docker
月光博客
月光博客
MongoDB | Blog
MongoDB | Blog
Vercel News
Vercel News
美团技术团队
博客园 - 叶小钗
I
InfoQ
Jina AI
Jina AI
博客园 - 司徒正美
雷峰网
雷峰网
B
Blog
Y
Y Combinator Blog
A
About on SuperTechFans
WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
大猫的无限游戏
大猫的无限游戏
Microsoft Security Blog
Microsoft Security Blog
Stack Overflow Blog
Stack Overflow Blog
腾讯CDC
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Recent Announcements
Recent Announcements
V
V2EX
N
Netflix TechBlog - Medium

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Asymmetric Rényi Problem and PATRICIA Tries
Michael Drmota, Abram Magner, Wojciech Szpankowski · 2016-05-06 · via cs.DS updates on arXiv.org

In 1960, Rényi asked for the number of random queries necessary to recover a hidden bijective labeling of n distinct objects. In each query one selects a random subset of labels and asks, what is the set of objects that have these labels? We consider here an asymmetric version of the problem in which in every query an object is chosen with probability p > 1/2 and we ignore "inconclusive" queries. We study the number of queries needed to recover the labeling in its entirety (the height), to recover at least one single element (the fillup level), and to recover a randomly chosen element (the typical depth). This problem exhibits several remarkable behaviors: the depth D_n converges in probability but not almost surely and while it satisfies the central limit theorem its local limit theorem doesn't hold; the height H_n and the fillup level F_n exhibit phase transitions with respect to p in the second term. To obtain these results, we take a unified approach via the analysis of the external profile, defined at level k as the number of elements recovered by the kth query. We first establish new precise asymptotic results for the average and variance, and a central limit law, for the external profile in the regime where it grows polynomially with n. We then extend the external profile results to the boundaries of the central region, leading to the solution of our problem for the height and fillup level. As a bonus, our analysis implies novel results for analogous parameters of random PATRICIA tries.